Of course, it's possible that my doubts will be reconciled in a video I haven't yet seen, so maybe this question will become moot. If anyone can clarify this point, I'd be grateful! I think there is an assumption here that doesn't quite work in real life, but I can't see what it is for now. I realize that the example is simplified for mathematical convenience (which is quite understanable) but it bothers me that just increasing the sample size makes us more certain. ![]() I'm sort of wondering what would be the correct sample size that would give us the best approximation in the real world. This way we could convince ourselves that the 2.2 L we have reserved for each men is enough because we can get the z-score of 0.2 L arbitrarily low. ![]() ![]() What I'm wondering about is that it seems that by calculating the result while increasing the sample size and the amount of trials maybe as well, it's possible to get arbitrarily close to the situation where the standard deviation is as small as desired. It seems to me that there is some kind of an underlying assumption here that makes the result suspect (and I'm talking mathematics, not about sweating and all those pesky real-life problems :-) ).
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